DataBright：走向分散数据所有权和可信计算的全球交换
DataBright: Towards a Global Exchange for Decentralized Data Ownership and Trusted Computation
摘要：
可以肯定的是，在可预见的未来，机器学习，特别是深度学习将仍然是数据和计算的饥渴。在本文中，我们要问：我们能否建立一个全球性的交换平台，让每个人都能贡献计算和数据来训练下一代机器学习应用？我们提出了一个早期的，但运行原型的DataBright，一个系统，把创建训练样本和共享的计算到投资机制。与大多数众包平台不同，贡献者在提交数据时获得报酬，DataBright在贡献者的数据或硬件被某人用于训练机器学习模型时支付红利。贡献者成为他们创建的数据集的股东。为了能够测量使用情况，还需要一个贡献者可以信任的计算平台。因此，DataBright合并了数据市场和可信计算市场。我们说明了可信计算可以创建一个人工智能市场，其中每个数据点都有一个确切的价值，应该支付给它的创建者。DataBright允许数据创建者保留其贡献的所有权，并为其附加可衡量的价值。数据的价值取决于其在DataBright计算市场上进行的后续分布式计算中的实用性。计算市场将任务和后续支付分配给池化硬件。这导致了去中心化AI云的创建。我们的实验表明，英特尔SGX等可信硬件可以添加到通常的ML管道中，而无需额外成本。我们使用此设置来编排分布式计算，从而创建计算市场。DataBright可在https://github.com/ds3lab/databright下载。
英文摘要：
It is safe to assume that, for the foreseeable future, machine learning, especially deep learning will remain both data- and computation-hungry. In this paper, we ask: Can we build a global exchange where everyone can contribute computation and data to train the next generation of machine learning applications? We present an early, but running prototype of DataBright, a system that turns the creation of training examples and the sharing of computation into an investment mechanism. Unlike most crowdsourcing platforms, where the contributor gets paid when they submit their data, DataBright pays dividends whenever a contributor's data or hardware is used by someone to train a machine learning model. The contributor becomes a shareholder in the dataset they created. To enable the measurement of usage, a computation platform that contributors can trust is also necessary. DataBright thus merges both a data market and a trusted computation market. We illustrate that trusted computation can enable the creation of an AI market, where each data point has an exact value that should be paid to its creator. DataBright allows data creators to retain ownership of their contribution and attaches to it a measurable value. The value of the data is given by its utility in subsequent distributed computation done on the DataBright computation market. The computation market allocates tasks and subsequent payments to pooled hardware. This leads to the creation of a decentralized AI cloud. Our experiments show that trusted hardware such as Intel SGX can be added to the usual ML pipeline with no additional costs. We use this setting to orchestrate distributed computation that enables the creation of a computation market. DataBright is available for download at https://github.com/ds3lab/databright.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
